• Korean Journal of Occupational Health Nursing
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• v.31 no.2
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• pp.77-85
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• 2022

Purpose: This study aimed to identify the influence of effort-reward imbalance and job satisfaction on turnover intention among hospital nurses. Methods: Data were collected from January 28 to February 10, 2022, from 237 nurses from five hospitals including clinics, general hospitals, and tertiary care hospitals located in B city. The collected data were analyzed using descriptive statistics, t-test, ANOVA, the Scheffe test, Pearson's correlation coefficients, and multiple linear regression analysis, using SPSS/WIN 26.0. Results: The average of the effort-reward ratio, an indicator of effort-reward imbalance, was 1.67±0.66, and 86.5% of the participants had a value of 1 or more. The mean job satisfaction and turnover intention were 3.32±0.48 and 3.69±0.89 on a 5-point scale, respectively. Multiple regression revealed that factors affecting turnover intention among hospital nurses included effort-reward imbalance (β=.30, p<.001) and job satisfaction (β=-.32, p<.001), and these variables explained 29.0% of turnover intention. Conclusion: These findings indicate that effort-reward imbalance and job satisfaction are associated with turnover intention. Therefore, to decrease the turnover intention of hospital nurses, interventions and policies should be prepared to resolve the nurse's effort-reward imbalance and increase job satisfaction at regional or national level hospitals.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.199-206
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• 2022

A malfunction or breakdown of a manufacturing facility leads to product defects and the suspension of production lines, resulting in huge financial losses for manufacturers. Due to the spread of smart factory services, a large amount of data is being collected in factories, and AI-based research is being conducted to predict and diagnose manufacturing facility breakdowns or manufacturing site efficiency. However, because of the characteristics of manufacturing data, such as a severe class imbalance about abnormalities and ambiguous label information that distinguishes abnormalities, developing classification or anomaly detection models is highly difficult. In this paper, we present an deep learning algorithm for anomaly detection of a manufacturing facility using reconstruction loss of CNN-based model and ananlyze its performance. The algorithm detects anomalies by relying solely on normal data from the facility's manufacturing data in the exclusion of abnormal data.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.235-242
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• 2022

One of the important research streams in the privacy literature for the past decade has been to discover factors affecting the decision-making process related to self-disclosure, called the cost-benefit analysis. However, although human behavior is greatly influenced by affective as well as cognitive factors, most of the factors found in previous studies are those with cognitive properties. Based on the awareness of this imbalanced situation, the study examines the role of affective factors on self-dislosure decision-making, especially SNS enjoyment and SNS fatigue. As a result of data analysis, the study finds that the influence of these affective factors is significant, and the influence of SNS enjoyment is greater than that of SNS fatigue. As for the relationship between the affective factors and the decision-making factors, the study finds that the positive affect(enjoyment) relates to only the positive evaluation factor(benefit) and the negative affect(fatigue) relates only the negative evaluation factor(cost), which demonstrate the congruent effect mechanism. Based on the result, the study discusses meaningful implications and suggestions for future studies.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.861-866
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• 2022

Seoul introduced the shared bicycle system, "Seoul Public Bike" in 2015 to help reduce traffic volume and air pollution. Hence, to solve various problems according to the supply and demand of the shared bicycle system, "Seoul Public Bike," several studies are being conducted. Most of the research is a strategic "Bicycle Rearrangement" in regard to the imbalance between supply and demand. Moreover, most of these studies predict demand by grouping features such as weather or season. In previous studies, demand was predicted by time-series-analysis. However, recently, studies that predict demand using deep learning or machine learning are emerging. In this paper, we can show that demand prediction can be made a little better by discovering new features or ordering the importance of various features based on well-known feature-patterns. In this study, by ordering the selection of new features or the importance of the features, a better coefficient of determination can be obtained even if the well-known deep learning or machine learning or time-series-analysis is exploited as it is. Therefore, we could be a better one for demand prediction.

• Journal of Adhesion and Interface
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• v.23 no.2
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• pp.17-24
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• 2022

Colloidal quantum dots (QDs) have gained attention for applications in quantum dot light emitting diodes (QLEDs) due to their high photoluminescence quantum yield, narrow emission spectra, and tunable bandgap. Nevertheless, non-radiative recombination induced by electron and hole imbalance deteriorates the device efficiency and stability. To overcome the problem, researchers have been trying to enhance hole transport properties of hole transporting layers (HTL) and/or slow down the electron injection in electron transport layer (ETL). Here, we summarize two approaches: i) development of interfacial materials between QD and ETL (or HTL); ii) engineering of HTL by blending or multi-layer approaches.

• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.251-256
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• 2022

In this paper, we propose the effects of coordinative locomotor training in body alignment of high school baseball players. Coordinative locomotor training was applied to 20 subjects in the experimental group for 30 minutes, 5timess aweek for 4 weeks. Body alignmen was measured using a formertic, and variables representing body alignment included trunk inclination, trunk imbalance, pelvic tilt, pelvic torson, kyphotic angle and lordotic angle. The results of this study were as follows: As for the Body alignment, there were significantly increased in kyphotic angle and lordotic angle in the experimental group. From the above results, it seems that coordinative locomotor training has a positive effects on the body alignment of high school baseball players. The coordinative locomotor training was able to produce confirmation that body alignment change in the case of effective exercise interventions in high school baseball players. Coordinative locomotor training is thought to be effective in preventing physical imbalance in high school baseball.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.2
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• pp.201-211
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• 2022

According to the recent change in the cybersecurity paradigm, research on anomaly detection methods using machine learning and deep learning techniques, which are AI implementation technologies, is increasing. In this study, a comparative study on data preprocessing techniques that can improve the anomaly detection performance of a GRU (Gated Recurrent Unit) neural network-based intrusion detection model using NGIDS-DS (Next Generation IDS Dataset), an open dataset, was conducted. In addition, in order to solve the class imbalance problem according to the ratio of normal data and attack data, the detection performance according to the oversampling ratio was compared and analyzed using the oversampling technique applied with DCGAN (Deep Convolutional Generative Adversarial Networks). As a result of the experiment, the method preprocessed using the Doc2Vec algorithm for system call feature and process execution path feature showed good performance, and in the case of oversampling performance, when DCGAN was used, improved detection performance was shown.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.2
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• pp.267-277
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• 2022

Due to the increasing proportion of cloud and remote working environments, various information security incidents are occurring. Insider threats have emerged as a major issue, with cases in which corporate insiders attempting to leak confidential data by accessing it remotely. In response, insider threat detection approaches based on machine learning have been developed. However, existing machine learning methods used to detect insider threats do not take biases and variances into account, which leads to limited performance. In this paper, boosting-type ensemble learning algorithms are applied to verify the performance of malicious insider detection, conduct a close analysis, and even consider the imbalance in datasets to determine the final result. Through experiments, we show that using ensemble learning achieves similar or higher accuracy to other existing malicious insider detection approaches while considering bias-variance tradeoff. The experimental results show that ensemble learning using bagging and boosting methods reached an accuracy of over 98%, which improves malicious insider detection performance by 5.62% compared to the average accuracy of single learning models used.

• Journal of Christian Education in Korea
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• v.69
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• pp.105-137
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• 2022

This paper is to study the characteristics of outdoor wilderness education and the impacts of outdoor wilderness experience on the participants in higher education. The first part of this paper addresses the common components of outdoor wilderness programs: adventure or self-discovery in disequilibrium, small groups for accountability in a temporary community, problem solving processes for decision making in real situations, solo time for integration in solitude, and leadership styles and role of the trip leaders. These elements of outdoor wilderness programs help the participants to achieve their goals according to its mission. The second part of this paper divides outdoor wilderness programs into three categories according to the objectives and outcomes of outdoor wilderness education: orientation programs for incoming students, personal leadership development programs, and professional training programs. The impacts of outdoor wilderness experiences on the participants of different programs in higher education were reviewed. Then guidelines for spiritual formation prorgams were proposed for Christian educators who are involved in wilderness programs in higher education to develop their practical wilderness experiences into holistic development programs according to its mission and goals.

• Journal of Broadcast Engineering
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• v.28 no.2
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• pp.238-241
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• 2023

Recently deep learning models are widely used for various tasks such as facial recognition and face editing. Their training process often involves a dataset with imbalanced age distribution. It is because some age groups (teenagers and middle age) are more socially active and tends to have more data compared to the less socially active age groups (children and elderly). This imbalanced age distribution may negatively impact the deep learning training process or the model performance when tested against those age groups with less data. To this end, we propose an age-controllable face synthesis technique using a feature decomposition to classify age from facial images which can be utilized to synthesize novel data to balance out the age distribution. We perform extensive qualitative and quantitative evaluation on our proposed technique using the FFHQ dataset and we show that our method has better performance than existing method.